RprobitB: Bayesian Probit Choice Modeling

Bayes estimation of probit choice models, both in the cross-sectional and panel setting. The package can analyze binary, multivariate, ordered, and ranked choices, and places a special focus on modeling heterogeneity of choice behavior among deciders. The main functionality includes model fitting via Markov chain Monte Carlo methods, tools for convergence diagnostic, choice data simulation, in-sample and out-of-sample choice prediction, and model selection using information criteria and Bayes factors. The latent class model extension facilitates preference-based decider classification, where the number of latent classes can be inferred via the Dirichlet process or a weight-based updating scheme. This allows for flexible modeling of choice behavior without the need to impose structural constraints. For a reference on the method see Oelschlaeger and Bauer (2021) <https://trid.trb.org/view/1759753>.

Package details

AuthorLennart Oelschläger [aut, cre] (<https://orcid.org/0000-0001-5421-9313>), Dietmar Bauer [aut] (<https://orcid.org/0000-0003-2920-7032>), Sebastian Büscher [ctb], Manuel Batram [ctb]
MaintainerLennart Oelschläger <oelschlaeger.lennart@gmail.com>
URL https://loelschlaeger.de/RprobitB/
Package repositoryView on CRAN
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RprobitB documentation built on Nov. 10, 2022, 5:12 p.m.